Modeling shape variability for full heart segmentation in cardiac computed-tomography images

Ecabert, Olivier; Peters, Jörg; Weese, Jürgen

doi:10.1117/12.652105

Cited by 35 publications

(31 citation statements)

References 10 publications

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“…The number of patients is significantly larger than those reported in the literature, for example, 10 in Schramm et al, 22 13 in Ecabert et al, 23 and 18 in Jolly. 17 The imaging protocols are heterogeneous with different capture ranges and resolutions.…”

Section: Methodscontrasting

confidence: 51%

Left ventricle endocardium segmentation for cardiac CT volumes using an optimal smooth surface

et al. 2009

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We recently proposed a robust heart chamber segmentation approach based on marginal space learning.1, 2 In this paper, we focus on improving the LV endocardium segmentation accuracy by searching for an optimal smooth mesh that tightly encloses the whole blood pool. The refinement procedure is formulated as an optimization problem: maximizing the surface smoothness under the tightness constraint. The formulation is a convex quadratic programming problem, therefore has a unique global optimum and can be solved efficiently. Our approach has been validated on the largest cardiac CT dataset (457 volumes from 186 patients) ever reported. Compared to our previous work, it reduces the mean point-to-mesh error from 1.13 mm to 0.84 mm (22% improvement). Additionally, the system has been extensively tested on a dataset with 2000+ volumes without any major failure.

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Section: Methodscontrasting

confidence: 51%

Left ventricle endocardium segmentation for cardiac CT volumes using an optimal smooth surface

et al. 2009

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“…The cardiac motion model was trained on additional 20 sequences (each with 10 frames). Our dataset is much larger than those reported in the literature, e.g., 13 in [38], 18 in [5], 27 in [39], and 30 in [40].…”

Section: Four Chambers Tracking Of the Heartcontrasting

confidence: 48%

Prediction Based Collaborative Trackers (PCT): A Robust and Accurate Approach Toward 3D Medical Object Tracking

Yang

Georgescu

Zheng

et al. 2011

IEEE Trans. Med. Imaging

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Abstract-Robust and fast 3D tracking of deformable objects, such as heart, is a challenging task because of the relatively low image contrast and speed requirement. Many existing 2D algorithms might not be directly applied on the 3D tracking problem. The 3D tracking performance is limited due to dramatically increased data size, landmarks ambiguity, signal drop-out or complex non-rigid deformation. In this paper we present a robust, fast and accurate 3D tracking algorithm: Prediction Based Collaborative Trackers (PCT). A novel onestep forward prediction is introduced to generate the motion prior using motion manifold learning. Collaborative trackers are introduced to achieve both temporal consistency and failure recovery. Compared with tracking by detection and 3D optical flow, PCT provides the best results. The new tracking algorithm is completely automatic and computationally efficient. It requires less than 1.5 seconds to process a 3D volume which contains millions of voxels. In order to demonstrate the generality of PCT, the tracker is fully tested on three large clinical datasets for three 3D heart tracking problems with two different imaging modalities: endocardium tracking of the left ventricle (67 sequences, 1134 3D volumetric echocardiography data), dense tracking in the myocardial regions between the epicardium and endocardium of the left ventricle (503 sequences, roughly 9000 3D volumetric echocardiography data), and whole heart four chambers tracking (20 sequences, 200 cardiac 3D volumetric CT data). Our datasets are much larger than most studies reported in the literature and we achieve very accurate tracking results compared with human experts' annotations and recent literature.

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“…Most of the previous approaches focus on boundary delineation based on active shape models (ASM), 5 active appearance models (AAM), 6,7 and deformable models. 3,[8][9][10][11][12] There are a few limitations inherent in these techniques: 1) Most of them are semi-automatic and manual labeling of a rough position and pose of the heart chambers is needed. 2) They are likely to get stuck in local strong image evidence.…”

Section: Related Work On Heart Segmentationmentioning

confidence: 99%

Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes

et al. 2008

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Multi-chamber heart segmentation is a prerequisite for quantification of the cardiac function. In this paper, we propose an automatic heart chamber segmentation system. There are two closely related tasks to develop such a system: heart modeling and automatic model fitting to an unseen volume. The heart is a complicated non-rigid organ with four chambers and several major vessel trunks attached. A flexible and accurate model is necessary to capture the heart chamber shape at an appropriate level of details. In our four-chamber surface mesh model, the following two factors are considered and traded-off: 1) accuracy in anatomy and 2) easiness for both annotation and automatic detection. Important landmarks such as valves and cusp points on the interventricular septum are explicitly represented in our model. These landmarks can be detected reliably to guide the automatic model fitting process. We also propose two mechanisms, the rotation-axis based and parallel-slice based resampling methods, to establish mesh point correspondence, which is necessary to build a statistical shape model to enforce priori shape constraints in the model fitting procedure.Using this model, we develop an efficient and robust approach for automatic heart chamber segmentation in 3D computed tomography (CT) volumes. Our approach is based on recent advances in learning discriminative object models and we exploit a large database of annotated CT volumes. We formulate the segmentation as a twostep learning problem: anatomical structure localization and boundary delineation. A novel algorithm, Marginal Space Learning (MSL), is introduced to solve the 9-dimensional similarity transformation search problem for localizing the heart chambers. After determining the pose of the heart chambers, we estimate the 3D shape through learning-based boundary delineation. Extensive experiments demonstrate the efficiency and robustness of the proposed approach, comparing favorably to the state-of-the-art. This is the first study reporting stable results on a large cardiac CT dataset with 323 volumes. In addition, we achieve a speed of less than eight seconds for automatic segmentation of all four chambers.

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Modeling shape variability for full heart segmentation in cardiac computed-tomography images

Cited by 35 publications

References 10 publications

Left ventricle endocardium segmentation for cardiac CT volumes using an optimal smooth surface

Left ventricle endocardium segmentation for cardiac CT volumes using an optimal smooth surface

Prediction Based Collaborative Trackers (PCT): A Robust and Accurate Approach Toward 3D Medical Object Tracking

Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes

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